Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Department of Information Technology

Due to the exponential growth in the size of available data, people often prefer direct answers in many cases, instead of a set of ranked documents while searching using a search engine. Furthermore, people often do not provide enough information regarding their search. Query expansion would help in such cases so that queries would be best understood by search engines. Although the queries are expanded, many a times, they end up with wrong results due to the entity mentions in the questions. Hence, we need to disambiguate named entities properly so as to choose the right mention. In this paper, a method is proposed for named entity disambiguation in order to help in query expansion. An adapted Lesk similarity measure is used to find the similarity between questions and the target Wikipedia disambiguation pages. The proposed approach has shown very good results in disambiguating organization- and miscellaneous-type entity mentions.

Department of Digital Communications

School of Information Technology and Engineering

Vellore Campus

Concurrency and Computation: Practice and Experience

Query expansion using named entity disambiguation for a question‐answering system

VIT Unitversity

Computer Communications

Predicting drug responsiveness with deep learning from the effects on gene expression of Obsessive–Compulsive Disorder affected cases

An Ontology is an absolute formal conceptualization of some realm of significance. Nowadays Ontologies play a vibrant part in Information Architecture, Biomedical Informatics, Electronic commerce, Software Engineering, Semantic Web, Knowledge management, Artificial Intelligence and etc. Huge number of Ontologies and extensive variety of Ontologies are available for every single domain. It creates very difficult to maintain and access all the existing Ontologies. Ontology merging is the solution to overcome this kind of problems. Ontology merging is a procedure of fetching two existing Ontologies as input and obtains a newly merged Ontology as output. The merged Ontology will have common concepts and relationships between two Ontologies. This paper presents how two Ontologies can be constructed and merged using Protege and Conexp tools with an example. © 2020, Springer Nature Switzerland AG.

Advances in Intelligent Systems and Computing Intelligent Systems Design and Applications

Construction and Merging of ACM and ScienceDirect Ontologies

In this paper, we presented precise and proficient techniques for measuring the human thalamus, medial dorsal and the pulvinar nucleus with magnetic resonance imaging (MRI). In spite of the fact that thalamic nuclei are not straightforwardly visible on traditional MRI image. We applied a novel and competent image pre-processing techniques to enhance the visual quality of MRI image. In addition to this we have used various segmentation algorithms to accurately extract entire thalamus from brain MRI images. Diffusion MRI is used to extract various nucleus of thalamus. Several optimal features such as textures, morphological are derived from thalamus and medial dorsal regions which are then used to train the artificial neural network model (ANN). Our artificial neural network model accurately classifies between schizophrenic and healthy subjects based on thalamic anatomy for larger sample sizes. Copyright © 2020 Inderscience Enterprises Ltd.

World University root node

Fulltext

International Journal of Internet Technology and Secured Transactions

Analysing thalamus and its sub nuclei in MRI brain image to distinguish schizophrenia subjects using back propagation neural network

Deep Learning is a revolution that has potential to cause tremendous technological upheavals across all walks of humanity. Here I present an application to translate alphabets of Indian Sign Language in real time and a pipeline to make similar applications. The main steps include Data set Generation, Model Training, Frame by Frame Classification. The application works in real time and in varying backgrounds and the user can type alphabets by doing corresponding gestures in front of webcam. While some other implementations require several expensive devices that user might need to wear, my application only needs a camera. © 2020 IEEE.

Department of Software and Systems Engineering

International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2020

Gesture Recognition Using Deep Learning

Technology is increasingly becoming a massive part of today's healthcare scenario. Technology has changed the way how patients communicate with doctors and not only that, but also how healthcare is administered. Artificial intelligence and Chabots are two groundbreaking technologies that have changed how patients and doctors perceive healthcare. To make healthcare system more interactive a diagnostic Chabot is designed and developed using latest algorithms in machine learning, decision tree algorithm to help the user to form a diagnosis of their condition based on their symptoms. The system will be fed with information pertaining to various diseases and using NLP, it will be able to understand the user query and give a suitable response. The system can be used for effective information retrieval in a similar manner like siri, alexa etc but the scope will be limited to disease diagnosis. © 2020 The Author(s).

Procedia Computer Science

Design and Development of Diagnostic Chabot for supporting Primary Health Care Systems

Due to the increasing in size of web information, it is needed to make a crawler with good scalability. Category based search engines are going to be the next generation search engines. Crawlers with good load balancing are required for Category based search engines. In this paper, we present a distributed web crawler architecture that holds goods for category support Search engine. Our category based search engine crawler splits the crawled data based upon the web page metadata into categories and distributes their storage and computing resources, with minimized communication cost. We also analyze the performance and scalability of the category based web crawler over traditional distributed web crawlers. © 2020 IEEE.

Crawler Framework for Category Search Engine

The most challenging task in any modern reasoning system is that it has been completely relied on automatic knowledge acquisition from the unstructured text and filtering out the structured information from it has turned out to be the most crucial task of Information Retrieval systems.In this paper, we have proposed a system that can recognize the potential named entities from the Twitter streams and link them to the appropriate real world knowledge entities. Besides, it has performed many semantic functions such as entity disambiguation, contextual similarity, type induction, and semantic labeling, to augment the semantic score of the entity and provide the rich entity feature space to quantitatively enhance entity retrieval accuracy. Nevertheless, we have leveraged a model to alleviate the entity imbalance present over the collected Twitter Streams and effectively utilized the contextual relatedness between the candidate entity sets. Eventually, we have proposed a probabilistic approach to deal with topic modeling and effectively disambiguate the entities by clustering the entities into its appropriate entity domain. The proposed Latent Dirichlet Allocation (LDA) model has been categorically distinguished the topics for clustering between the candidate entities and fix the exact true mentions occurred in the Knowledge Base such as DBpedia. We have also demonstrated the performance and accuracy rate of the proposed system and evaluated the results with the collected Twitter Streams for the month of August, 2016. The empirical results have shown that it has outperformed the existing state-of-the-art systems and proved that the proposed system given here has gradual accuracy rate against the conventional systems. © 2020, Indian Academy of Sciences.

Department of Computer Applications

Sadhana - Academy Proceedings in Engineering Sciences

An algorithmic approach to rank the disambiguous entities in Twitter streams for effective semantic search operations

The Security available in personal computers and laptops are not possible in mobile communication, since there is no controlling software such as an operating system. The European Union General Data Protection Regulation (GDPR) will require many organisations throughout the European Union to comply with new requirements that are intended to protect their user's personal data. The responsibilities of the organizations and the penalties related to the protection of personal data of the users are proved to be both organisationally and technically challenging. Under the GDPR's 'privacy by design' and 'privacy by default' requirements, organizations need to prove that they are in control of user data and have taken steps to protect it. There are a large number of organizations that makes use of mobile devices to process personal data of their customers. GDPR mandates that the organization shall be able to manage all devices that handles sensitive data so that the company can implement group updates, restrict apps and networks, and enforce security measures. In this work, we propose a Mobile Device Management solution using the built-in frameworks of Android and iOS mobile platforms which is compatible and incorporates GDPR articles relevant to a small to medium sized organization. © 2020 IEEE.